Leveraging variational autoencoders for multiple data imputation

Breeshey Roskams-Hieter, Jude Wells, Sara Wade

Proceedings of the European Conference on Machine Learning (ECML-PKDD) 2023, Springer Lecture Notes in Computer Science

Fast deep mixtures of Gaussian process experts

C. Etienam, K. Law, S. Wade, V. Zankin

Machine Learning (to appear), Springer, 2023

Bayesian cluster analysis

Sara Wade

Phil. Trans. R. Society, vol. 381(20220149), 2023

Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

A.K. Leist, M. Klee, J.H. Kim, D.H Rehkopf, S.P.A Bordas, G. Muniz-Terrera, S. Wade

Science Advances, 2022

Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models

Mica Teo, Sara Wade

Springer Proceedings in Mathematics and Statistics, New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions, 2022, pp. 45-56

Colombian women's life patterns: A multivariate density regression approach

S. Wade, R. Piccarreta, A. Cremaschi, I. Antoniano Villalobos

Bayesian Analysis, vol. 17, 2022, pp. 405-433

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data

W. Yu, S. Wade, H.D. Bondell, L. Azizi

Journal of Computational and Graphical Statistics, vol. 32(2), 2022, pp. 588-600

Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models

C. Gadd, S. Wade, A. Shah

Machine Learning, vol. 110, 2021, pp. 1105-1143

Enriched mixtures of Gaussian process experts.

C. Gadd, S. Wade, A. Boukouvalas

Proceedings of Machine Learning Research, International Conference of Artificial Intelligence and Statistics (AISTATS), vol. 108, 2020, pp. 3144-3154

Posterior inference for sparse hierarchical non-stationary models

K. Monterrubio-Gomez, L. Roininen, S. Wade, T. Damoulas, M. Girolami

Computational Statistics & Data Analysis, vol. 148, 2020, pp. 1-22

Bayesian cluster analysis: point estimation and credible balls (with Discussion)

Sara Wade, Zoubin Ghahramani

Bayesian Analysis, vol. 13, International Society for Bayesian Analysis, 2018, pp. 559 -- 626

Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics

A. Prestia, A. Caroli, S. Wade, et al.

Alzheimer's & Dementia, vol. 11, 2015, pp. 1191-1120

A Bayesian nonparametric regression model with normalized weights; A study of hippocampal atrophy in Alzheimer's disease

I. Antoniano-Villalobos, S. Wade, S. G. Walker

Journal of American Statistical Association, vol. 109, 2014, pp. 477-490

Alzheimer's disease biomarkers as outcome measures for clinical trials in MCI

A. Caroli, A. Prestia, S. Wade, et al.

Alzheimer's Disease \& Associated Disorders, vol. 29, 2014, pp. 101-109

Improving prediction from Dirichlet process mixtures via enrichment

S. Wade, D.B. Dunson, S. Petrone, L. Trippa

Journal of Machine Learning Research, vol. 15, 2014, pp. 1041-1071

A predictive study of Bayesian nonparametric regression models

S. Wade, S. G. Walker, S. Petrone

Scandinavian Journal of Statistics, vol. 41, 2014, pp. 580-605

An enriched conjugate prior for Bayesian nonparametric inference

S. Wade, S. Mongelluzzo, S. Petrone

Bayesian Analysis, vol. 6, 2011, pp. 1-28

Bayesian nonparametric regression through mixture models

Sara Wade

2013

Contributed Discussion: Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data by Kidd and Katzfuss

L. Azizi, S. Wade, W. Yu

Bayesian Analysis, 2022

Contributed Discussion: Centered Partition Processes: Informative Priors for Clustering by Paganin et al.

I. Antoniano-Villalobos, C. Villa, S. Wade

Bayesian Analysis, 2021

Contributed Discussion: Centered Partition Processes: Informative Priors for Clustering by Paganin et al

A. Avalos-Pacheco, R. De Vito, S. Wade

Bayesian Analysis, 2021

Mixture of Gaussian Process Experts with SMC^2

T. Harkonen, S. Wade, K. Law, L. Roininen

2023

Shared clustering across single-cell RNA sequencing datasets with the hierarchical Dirichlet process

J. Liu, S. Wade, N. Bochkina

2023

On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach

K. Monterrubio-Gomez, S. Wade

2023

Dependent Bayesian mixture models: A predictive comparison and survey

S. Wade, V. Inacio

2023

Bayesian Statistics and New Generations: BAYSM 2018, Warwick, UK, July 2-3, Selected Contributions.

Selected Contributors

Springer Proceedings in Mathematics and Statistics, R. Argiento, D. Durante, S. Wade, 2019